Internet of Things Webinar Series

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Internet of Things – Webinar Series

© 2016 SAP SE or an SAP affiliate company. All rights reserved. 2 Customer

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presentation, or to develop or release any functionality mentioned therein. This document, or any related presentation

and SAP's strategy and possible future developments, products and or platforms directions and functionality are all

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document is not a commitment, promise or legal obligation to deliver any material, code or functionality. This

document is provided without a warranty of any kind, either express or implied, including but not limited to, the implied

warranties of merchantability, fitness for a particular purpose, or non-infringement. This document is for informational

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document, except if such damages were caused by SAP´s willful misconduct or gross negligence.

All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ

materially from expectations. Readers are cautioned not to place undue reliance on these forward-looking statements,

which speak only as of their dates, and they should not be relied upon in making purchasing decisions.

Legal disclaimer


SAP Internet of Things – Webinar Series

SAP IoT Overview Nils Herzberg Mar 8

SAP Logistics Hub Uwe Kürsten Mar 22

Create new business models based on vehicle

data analysis with SAP Vehicle Insight Mirjam Metzler Mar 29

SAP Asset Intelligent Network Mathew Easley/Dirk Kempf Apr 5

SAP Predictive Maintenance Simon Lee Mar 21

SAP HANA Cloud Platform & HANA Cloud

Integration Alex Braun /

Piyush Gakhar Mar 15

Moderator: Jos Houben

SAP IoT Application Services Harry Lube Apr 26


Where are maintenance and service today? Different stakeholders with different concerns

How can I improve my

product’s reliability and

uptime for my customer?

How can I reduce my

warranty costs?

How can I generate new

service-revenue streams?

How can I provide the best service at the right time?

How can I utilize my

maintenance budget better?

How can I prevent unplanned

asset downtime? OEM Operator

Service provider

How can I prioritize maintenance

activities and operate with

reduced risks?


Companies are moving

from a reactive to a

proactive approach to

maintenance.

An opportunity is available

for organizations to

leverage machine data for

better business insights.

Where are maintenance and service heading? Organizations are maturing their maintenance strategies

Reactive

Preventive

Condition-

based

Predictive

Wait until a

machine fails

and then

undertake

maintenance.

Perform

maintenance at

regular intervals,

based on

observations of

abnormalities.

Continuously

observe the

status of assets

and react to

predefined

conditions and

events.

Advanced analytics of

operational and

business data helps

determine the condition

of specific equipment to

predict when to perform

maintenance.


Danger

Maintain speed –

avoid vehicle on

left

IoT provides a proactive and after-the-fact view of

business processes.

Digitization of enterprises with

the Internet of Things (IoT) Historical and

real-time data

Data-driven view

of business

Change

manage-

ment

Why is the trend an advantage? Move from reactive to proactive business processes

. . . To proactive decision making From reactive behavior . . .

SAP today provides post-mortem view on business processes


75% Percent of businesses will be

digital by 20202

Reduction in the price of

sensors, microprocessors, and

wireless technologies over the

past four years1

80%

For the IoT to become

mainstream2

2–5 years

1 1

1 0 0

0 0 1

1

1 1

Why now? The world is more connected, enabling digitization of businesses

1) Source: Economist Intelligence Unit – ”The Rise of the Machines” 2) Source: Internal SAP study provided by Boston Consulting Group, 2016


10%–40% Reduction of maintenance costs of factory equipment

Up to 50% Reduction of equipment downtime

3%–5% Reduction of equipment capital investment by extending the useful life of machinery

US$ 630 billion Potential economic impact

annually in 2025

5%–10% Reduction of maintenance costs

3%–5% Increase in output by avoiding unplanned outages

5%–10% Reduction of equipment capital investment by extending the useful life of machinery

Manufacturing

Work sites Oil and gas, mining,

and construction

US$ 360 billion Potential economic impact

annually in 2025

Source: The Internet of Things: Mapping the Value Beyond the Hype, McKinsey Global Institute, June 2015 Video Trenitalia: Creating a System of Maintenance Management Powered by SAP HANA

What is the business benefit? Improving asset reliability promises large savings opportunities

http://www.mckinsey.com/insights/business_technology/The_Internet_of_Things_The_value_of_digitizing_the_physical_world

https://www.youtube.com/watch?v=583aGe0xIGY




How does such a solution generate value? Business value to OEMs and operators

Increased first-visit fix rate By understanding the detailed situation that has lead to an asset failure

better, OEMs and operators can identify the right skills and spare parts

that are relevant for corrective actions.

Improve service profitability OEMs can offer higher margin services that include remote monitoring

and simultaneously resolve more calls remotely to reduce service costs.

Reduced maintenance cost Maintenance schedules can now be driven by sensor information that

allows operators to perform only the required interventions at exactly the

right time.

Warranty cost reduction Service experts can conduct root cause analysis on their products in use

that can be leveraged to improve the design and production quality to avoid

further warranty claims.

Improved equipment effectiveness With the use of machine learning algorithms (such as anomaly detection or

lifecycle analysis) asset operators can predict failures early and implement

corrective actions, which significantly increases the availability of critical

assets.

Business model innovation OEMs are now able to offer new service business models for their products

that were previously impossible, such as full-service agreements or pay per

use.


Insight Action

IT/OT* Convergence

• Big Data ingestion

• Big Data infrastructure

• Merging sensor data

with business

information

Maintenance activities

• Prioritized maintenance

and service activities

• Optimized warranty

and spare parts

management

• Prescriptive

Maintenance

• Quality improvements

Data analysis

• Root cause analysis

• Asset health monitoring

• Machine learning

• Anomaly detection

• Triggering of corrective

actions

Connected assets

• Onboarding

• Connectivity

• Device management

• Security

Business Value

• Customer experience

• Increased quality

• Lower costs

• Operational efficiency

• R&D effectiveness

• Material procurement

Sensor Data Insight Action Outcome

SAP Predictive Maintenance and Service solution From sensor to outcome

*) OT = operational technology


Many companies are optimizing today Across many industries

Compressor manufacturer

Equipment manufacturer Train operator

Utilities company The business model was changed from

selling compressors to selling compressed

air. The results were improved capabilities

for compressor stations and a move from

unplanned to planned maintenance.

Some 40% of maintenance effort is for

corrective maintenance. Implemented

remote train diagnostics, engineering

rules, and predictive models, which

resulted in lower maintenance costs,

less effort, and higher passenger

satisfaction.

The business requirement was to improve

asset performance. Predictive

maintenance resulted in a reduction in

equipment failures, while improving

reliability and customer satisfaction.

The goal was to strengthen company brand

by improving product quality and reliability

through analysis of equipment telematics

data. The results were reduced warranty

costs, a shortened detection-to-correction

cycle, and improved equipment uptime.


Failure rate Burn-in

"infant mortality" Wear-out Normal life

Asset lifetime

Emerging Issues Detection

Early identify, monitoring and management

of emerging asset issues using exploration,

root cause and warranty analytics

Predictive Maintenance and Service

(AHCC)

Holistic management of asset health and

decision support for maintenance schedule

and resource (e.g. spare parts) optimization

based on health scores, anomaly detection

and spectral analysis

Asset Investment Optimization and

Simulation

Analyze remaining useful life of assets to

optimally plan for new investments based on

business needs, asset health and risk of

failure.

SAP ERP, S4HANA, CRM, C4C

Vision for Connected Asset Lifecycle Management

Connected Asset Life Cycle addresses warranty, maintenance and investment related business challenges

throughout the asset lifecycle


Geo-

Spatial

Insight

Provider

Asset

Explorer

Insight

Provider

Key

Figure

Insight

Provider

3D

Visualizat

ion

Insight

Provider

Predictive Maintenance and Service

Data Management Data Processing

Work

Activities

Insight

Provider

Derived

Signal

Insight

Provider

SAP HANA SAP IQ SAP Data Services* SAP ESP*

*Optional components

Asset Health Control Center (AHCC)

Extendable

by additional

Custom

Insight

Provider &

Custom Data

Services

Predictive Maintenance and Service On-Premise Edition

Connected

Assets

Devices,

machines,

sensors

Integration

possible with

Telit

DeviceWise,

SAP PCo

Process

Automation

Closed-loop

business

process

integration

into PM and

MRS

IT

Integration

Remaining

Useful Life

Prediction

Distance-

Based

Failure

Analysis

Anomaly Detection with Principal Component Analysis

Data Science Services Insight Provider

OT (Device)

Integration

IoT

Ap

pli

cati

on

s

Op

era

tio

nali

zed

An

aly

tic

s a

nd

Data

Scie

nce

Serv

ices

IoT

Base

Serv

ices

Big

Data

Pla

tfo

rm

Asset Health Fact Sheet (AHFS)

Components

Insight

Provider

SAP Products and Capabilities Driving Scale - Business Applications Built on Modular Analytics

Data Fusion

Storage Data ingestion


Health status at a glance

Health status of the complete fleet

Aggregated from component health scores

Based on out-of-the-box machine learning

Derived Signals Management

Personal and extensible

Flexibly composed by insight providers

Drill-down into 1 machine

Integrated into operational processes

E.g. close-loop integration for services

via Multi-Resource Scheduling (MRS)

SAP Products and Capabilities Asset Health Control Center: Supervise Machine Health and Act on Issues


SAP Products and Capabilities Data Science Defined – Applicable Use Cases

Anomaly Detection

• Apply Principal Component Analysis to

sensor data to identify the ‘outliers’,

those items or events which do not

conform to the expected pattern

• Automatic detection of multivariate

anomalies, which could lead to failures

in components

Distance-Based

Failure Analysis

• Store ideal state “snapshot” of

component, compare against regular

capture of current state snapshot

• Comparison analysis can result in early

malfunctions in order to reduce

downtime

Remaining Useful

Life Prediction

• Conduct Weibull Life Time Analysis

on captured repair data to estimate

component lifetime

• Calculate remaining useful life and

probability of failure for each

machine/component and store

scores in time series storage

P Q

PCA


SAP Products and Capabilities Backend Integration

MRS integration features

• Order management

• Capacity Planning

• Maintenance scheduling

• Prediction based rescheduling

• Material availability check

PdMS PdMS MRS

Create notification in

business system

Optimize

maintenance

schedule

Track work

activites

• Create work activities for

identified issues in Asset

Health Fact Sheet

• Business system can be

PM / CS / CRM / C4C

• Track created work

activity in Work Activity

Insight Provider


Insight Providers shipped as part of the PdMS Base

Package extend AHCC and AHFS with new features

Insight Provider Name Explanation

Asset Explorer Insight Provider Asset Explorer selects and displays assets with its key figures and attributes. It includes hierarchical component

composition and global filtering.

Geo-Spatial Insight Provider Geo-Spatial Insight Provider provides a map view that visualizes and interprets data geographically. It supports

tool tips, layering, color coding, selection, geo-fencing and is map provider agnostic.

Key Figure Insight Provider Key Figure Insight Provider supports user defined key figures (e.g. KPIs) and provides the dynamic display of it

according to the global filter.

3D Visualization Insight Provider 3D Visualization of telematics data and derived signals.

Work Activity Insight Provider Provides the ERP Backend integration (retrieving and creation) of Work Orders and Notifications and supports the

integration to a scheduling solution (MRS will be preconfigured).

Components Insight Provider Components Insight Provider provides a hierarchical list of components with attributes such as health status,

health scores, and fey figures.

Derived Signal Insight Provider

Derived Signal Insight Provider generates and displays derived (calculated) signals (e.g. Health Scores, Alerts)

from raw data by using HANA Rules Framework or Data Science Services. It also provides a drill down to further

explanatory details


Insight Provider Asset Explorer Insight Provider

Business Purpose Asset Explorer selects and displays assets with its attributes.

It supports hierarchical components and filtering.

Features

Display of assets with its attributes

Configurable list of displayed attributes

Filtering assets by configurable dimensions

Filtering assets by functional locations in the functional location

hierarchy


Insight Provider Geo-Spatial Insight Provider

Business Purpose The Geo-Spatial Insight Provider provides a map view that

visualizes and interprets data geographically. It is meant for use

cases with assets in a distributed service area.

Features

Detailed information for displayed assets via tooltip

Support for map overlays that can be toggled individually

Color coding of displayed assets

Single selection of assets to launch AHFS

Geo-fencing and selection of geo-fenced assets

Map provider agnostic


Insight Provider Key Figure Insight Provider

Business Purpose The Insight Provider for Key Figures supports user defined key figures.

Key figures are aggregated according to the global filter.

Features

Flexible key figure calculation by using HANA stored procedures.

Support of key figure sets that bundle key figures into sets that are

always added together to AHCC or AHFS.


Insight Provider 3D Visualization Insight Provider

Business Purpose The Insight Provider for 3D visualization allows users to quickly

move through a set of parameters to visually detect reoccurring

patterns or correlations to find leading indicators to failure across

multiple assets.

Features

Able to select a sensor from a list and see the values (sensor

readings, derived signals) along the Y axis.

The time along the X axis and different machines as the Z axis.

The Insight Provider is able to overlay maintenance events on

the 3D chart to filter the assets.

Detailed information for displayed assets via tooltip.

Interactive setting of thresholds on 3D chart.


Insight Provider Work Activity Insight Provider

Business Purpose The Work Activity Insight Provider enables the back-end integration to

the business systems. It performs an action out of the prediction or

based on the status of a machine or its component.

Features


Detailed information and current status of service notifications and

work orders.

Detailed view of the scheduled maintenance activities and its

assigned resources from AHCC.

Creation of work orders from AHFS.


Insight Provider Components Insight Provider

Business Purpose Components Insight Provider provides a hierarchical list of

components with attributes such as health status, health scores, and

fey figures.

Features

Hierarchical component view with attributes



Insight Provider Derived Signal Insight Provider

Business Purpose Derived Signal Insight Provider generates and displays derived

(calculated) signals (e.g. Alerts) from raw data by using HANA Rules

Framework or Data Science Services.

Features


Drill down to further explanatory details


The PdMS On-Premise Edition can be extended with customer-

specific analyses that seamlessly integrate into the application

The Extensibility Concept allows to extend existing Insight Providers by:

Extend the logic of existing Insight Providers

Define customer-specific key figures (e.g. KPIs) and bundle key figures into sets that are always added together to the

analysis.

Define customer-specific rules for creation of alerts and other derived signals that can be consumed by other Insight Provider.

Customize out-of-the-box data science algorithms

Create models by training one of the provided data science algorithms with customer data.

Provide new data science algorithms

Extend the existing data science services by creating own analysis packages using the programming library R.

Provide new Insight Provider

Extend the analytical capabilities by adding new Insight Providers that implement customer-specific analyses.

Custom Insight Providers can be added to the Asset Health Control Center and Asset Health Factsheet in the same way the

pre-defined Insight Providers do and seamlessly integrate into the application.


Discovery Workshop

Data Modeling

Validation Iterations

O P T I O N A L P R O O F O F C O N C E P T

G O - L I V E

I M P L E M E N T A T I O N

( S A P S E R V I C E S / C D )

< 3 months

PdMS Go-Live

CDP (optional)

Extending to Meet Business Needs Optional PoC is an opportunity to increase customer value proposition

Implementation

• Understand

and reframe the

problem(s)

• Ideate possible

solutions

• Ensure

technical

feasibility

• Determine

standard out-

of-the-box fit

• Evaluate

technical

feasibility

• Weekly iterations

to collect feedback

• Pass acceptance

tests and/or

success criteria

• Prepare data

• Model data

• Generate

derived signal

models

• Standard

Implementation

• Based on SAP

standard PdMS

product

• Custom

development of

new functionality

• Go-Live Support

• Standard Support

• SAP CD Support

(optional)

• Application

Management

Service (AMS)

(optional)

S T A R T

27 © 2016 SAP SE or an SAP affiliate company. All rights reserved.

Webinars, Recordings & Presentations

available @

http:sap.com/k4u


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In particular, SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or any related presentation, or to develop

or release any functionality mentioned therein. This document, or any related presentation, and SAP SE’s or its affiliated companies’ strategy and possible future

developments, products, and/or platform directions and functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time

for any reason without notice. The information in this document is not a commitment, promise, or legal obligation to deliver any material, code, or functionality. All forward-

looking statements are subject to various risks and uncertainties that could cause actual results to differ materially from expectations. Readers are cautioned not to place

undue reliance on these forward-looking statements, which speak only as of their dates, and they should not be relied upon in making purchasing decisions.

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